Welcome to the Manchester Summer School

Welcome to the Manchester Summer School#

Spatial Analysis of Planning Applications and Sites of Biological Interest in Greater Manchester#

The aim of this project is to investigate the extent to which future property development threatens biodiversity in the Greater Manchester region. We are using spatial data supplied by the Greater Manchester Combined Authority and the Greater Manchester Ecology Unit to indentify if Sites of Biological Importance within the Greater Manchester region are at risk from future residential and/or commercial property development. We are using Jupyter and GitHub Pages to write, build and publish a website that incorporates discussion of the methodology and results with the code and its outputs.

Use the map below to investigate the spatial data we are using for this project:

Hide code cell source 
import geopandas as gpd
import pandas as pd

# Load and join GMCA housing, industrial and office supply data

housing_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Housing Land Supply GIS.shp")
industrial_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Industrial-warehousing Land Supply GIS.shp")
offices_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Offices Land Supply GIS.shp")

total_supply_gdf = pd.concat(
    [housing_supply_gdf, industrial_supply_gdf, offices_supply_gdf]
)

# Load and tidy GMEU Sites of Biological Importance data

sbi_gdf = gpd.read_file("data/gmeu_data/gm_sbi.shp")

sbi_gdf["Category"] = "Site of Biological Importance"

sbi_gdf = sbi_gdf.rename(columns = {"district": "LAName", "site_nam": "SiteRef"})

# Join GMCA and GMEU data

full_data_gdf = pd.concat(
    [total_supply_gdf, sbi_gdf[["SiteRef", "LAName", "Category", "geometry"]]]
)

# Explore the data

full_data_gdf.explore("Category", cmap="tab10", tiles="CartoDB positron")
Make this Notebook Trusted to load map: File -> Trust Notebook

Project tasks#

  • You need to define a heuristic to decide if a Site of Biological Importance (SBI) is ‘at risk’ or not. Write an algorithm that labels all SBIs in the dataset ‘at risk’ or ‘not at risk’. (Hint: The quickest way to do this is to examine distances between centroids - but you will need to make assumptions about what you can represent with centroids, and realise that not everything in this data can be represented with a centroid, e.g., a canal or river.)

  • According to your heuristic, how many SBIs are ‘at risk’ in total? How much does this vary when you adjust your heuristic?

  • Which borough in Greater Manchester has the most sites ‘at risk’?

  • Looking at all sites ‘at risk’, does risk stem more from residential development or from commercial development? Does it vary by borough?

Contents